Overview

Dataset statistics

Number of variables12
Number of observations13406
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory96.0 B

Variable types

Categorical1
Numeric11

Alerts

Symbol has a high cardinality: 3110 distinct values High cardinality
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
mean-return is highly correlated with VaR (95%)High correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with mean-return and 1 other fieldsHigh correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG ScoreHigh correlation
Social Pillar Score is highly correlated with ESG ScoreHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
mean-return is highly correlated with semi-variance (down) and 1 other fieldsHigh correlation
semi-variance (down) is highly correlated with mean-return and 1 other fieldsHigh correlation
kurtosis is highly correlated with skewHigh correlation
skew is highly correlated with kurtosisHigh correlation
VaR (95%) is highly correlated with mean-return and 1 other fieldsHigh correlation
semi-variance (down) is highly skewed (γ1 = 69.10115461) Skewed
Symbol is uniformly distributed Uniform
ESG Score has unique values Unique
semi-variance (down) has unique values Unique
kurtosis has unique values Unique
Environmental Pillar Score has 5016 (37.4%) zeros Zeros
D(ESG, VaR) has 2292 (17.1%) zeros Zeros

Reproduction

Analysis started2022-09-26 12:40:37.232833
Analysis finished2022-09-26 12:40:50.366802
Duration13.13 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Symbol
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3110
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size104.9 KiB
WSM.N
 
7
SMTC.OQ
 
7
DLTR.OQ
 
7
DLTH.OQ
 
7
OLLI.OQ
 
7
Other values (3105)
13371 

Length

Max length8
Median length7
Mean length6.08652842
Min length3

Characters and Unicode

Total characters81596
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique253 ?
Unique (%)1.9%

Sample

1st row360.AX
2nd row360.AX
3rd rowA.N
4th rowA.N
5th rowA.N

Common Values

ValueCountFrequency (%)
WSM.N7
 
0.1%
SMTC.OQ7
 
0.1%
DLTR.OQ7
 
0.1%
DLTH.OQ7
 
0.1%
OLLI.OQ7
 
0.1%
BOX.N7
 
0.1%
ENS.N7
 
0.1%
CAL.N7
 
0.1%
HIBB.OQ7
 
0.1%
GME.N7
 
0.1%
Other values (3100)13336
99.5%

Length

2022-09-26T13:40:50.409813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wsm.n7
 
0.1%
bby.n7
 
0.1%
gco.n7
 
0.1%
hqy.oq7
 
0.1%
apog.oq7
 
0.1%
vsto.n7
 
0.1%
rh.n7
 
0.1%
casy.oq7
 
0.1%
tgi.n7
 
0.1%
urbn.oq7
 
0.1%
Other values (3100)13336
99.5%

Most occurring characters

ValueCountFrequency (%)
.13406
16.4%
O9062
 
11.1%
N8595
 
10.5%
Q7378
 
9.0%
C3648
 
4.5%
A3504
 
4.3%
S3159
 
3.9%
T3109
 
3.8%
R3103
 
3.8%
I2437
 
3.0%
Other values (25)24195
29.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter68142
83.5%
Other Punctuation13406
 
16.4%
Lowercase Letter38
 
< 0.1%
Decimal Number6
 
< 0.1%
Connector Punctuation4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O9062
13.3%
N8595
 
12.6%
Q7378
 
10.8%
C3648
 
5.4%
A3504
 
5.1%
S3159
 
4.6%
T3109
 
4.6%
R3103
 
4.6%
I2437
 
3.6%
E2296
 
3.4%
Other values (16)21851
32.1%
Lowercase Letter
ValueCountFrequency (%)
a23
60.5%
b10
26.3%
p4
 
10.5%
q1
 
2.6%
Decimal Number
ValueCountFrequency (%)
32
33.3%
62
33.3%
02
33.3%
Other Punctuation
ValueCountFrequency (%)
.13406
100.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin68180
83.6%
Common13416
 
16.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
O9062
13.3%
N8595
 
12.6%
Q7378
 
10.8%
C3648
 
5.4%
A3504
 
5.1%
S3159
 
4.6%
T3109
 
4.6%
R3103
 
4.6%
I2437
 
3.6%
E2296
 
3.4%
Other values (20)21889
32.1%
Common
ValueCountFrequency (%)
.13406
99.9%
_4
 
< 0.1%
32
 
< 0.1%
62
 
< 0.1%
02
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII81596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.13406
16.4%
O9062
 
11.1%
N8595
 
10.5%
Q7378
 
9.0%
C3648
 
4.5%
A3504
 
4.3%
S3159
 
3.9%
T3109
 
3.8%
R3103
 
3.8%
I2437
 
3.0%
Other values (25)24195
29.7%

Year
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.82478
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-09-26T13:40:50.471826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12018
median2019
Q32020
95-th percentile2021
Maximum2022
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.628435498
Coefficient of variation (CV)0.0008066254754
Kurtosis-1.051898112
Mean2018.82478
Median Absolute Deviation (MAD)1
Skewness-0.1894160148
Sum27064365
Variance2.651802171
MonotonicityNot monotonic
2022-09-26T13:40:50.526839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20202848
21.2%
20192533
18.9%
20212310
17.2%
20182228
16.6%
20171948
14.5%
20161385
10.3%
2022154
 
1.1%
ValueCountFrequency (%)
20161385
10.3%
20171948
14.5%
20182228
16.6%
20192533
18.9%
20202848
21.2%
20212310
17.2%
2022154
 
1.1%
ValueCountFrequency (%)
2022154
 
1.1%
20212310
17.2%
20202848
21.2%
20192533
18.9%
20182228
16.6%
20171948
14.5%
20161385
10.3%

ESG Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13406
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.83428893
Minimum0.917084056
Maximum94.44445553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-09-26T13:40:50.602856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.917084056
5-th percentile13.04270178
Q123.67498386
median34.03591873
Q349.41499446
95-th percentile74.19126267
Maximum94.44445553
Range93.52737147
Interquartile range (IQR)25.74001061

Descriptive statistics

Standard deviation18.65358299
Coefficient of variation (CV)0.4930337935
Kurtosis-0.2473272893
Mean37.83428893
Median Absolute Deviation (MAD)12.05213218
Skewness0.6749380618
Sum507206.4773
Variance347.9561585
MonotonicityNot monotonic
2022-09-26T13:40:51.014949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.081018031
 
< 0.1%
76.193723211
 
< 0.1%
52.869171671
 
< 0.1%
17.157747221
 
< 0.1%
16.687266031
 
< 0.1%
24.010580541
 
< 0.1%
27.119638891
 
< 0.1%
27.499813931
 
< 0.1%
47.805050661
 
< 0.1%
50.07869191
 
< 0.1%
Other values (13396)13396
99.9%
ValueCountFrequency (%)
0.9170840561
< 0.1%
0.9908114171
< 0.1%
1.1460324561
< 0.1%
1.5178297071
< 0.1%
1.5375607681
< 0.1%
1.7404618571
< 0.1%
2.0562790721
< 0.1%
2.3700783311
< 0.1%
2.4429025551
< 0.1%
2.5235361731
< 0.1%
ValueCountFrequency (%)
94.444455531
< 0.1%
93.539125651
< 0.1%
93.327841241
< 0.1%
92.806508361
< 0.1%
92.619923141
< 0.1%
92.315162631
< 0.1%
92.14299521
< 0.1%
91.905084061
< 0.1%
91.855033491
< 0.1%
91.476861231
< 0.1%

Environmental Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7250
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.44250285
Minimum0
Maximum97.98022505
Zeros5016
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-09-26T13:40:51.101969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.104938272
Q335.48549651
95-th percentile76.57532438
Maximum97.98022505
Range97.98022505
Interquartile range (IQR)35.48549651

Descriptive statistics

Standard deviation26.00075419
Coefficient of variation (CV)1.271896812
Kurtosis0.1328687375
Mean20.44250285
Median Absolute Deviation (MAD)7.104938272
Skewness1.161204355
Sum274052.1932
Variance676.0392187
MonotonicityNot monotonic
2022-09-26T13:40:51.188988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05016
37.4%
21.15270351121
 
0.9%
1.74058178452
 
0.4%
1.77177177239
 
0.3%
1.60882140333
 
0.2%
20.3940886720
 
0.1%
17.5356921220
 
0.1%
4.94505494518
 
0.1%
37.3170731718
 
0.1%
6.30252100816
 
0.1%
Other values (7240)8053
60.1%
ValueCountFrequency (%)
05016
37.4%
0.0277777781
 
< 0.1%
0.0869565221
 
< 0.1%
0.0885935771
 
< 0.1%
0.0916722961
 
< 0.1%
0.1421464111
 
< 0.1%
0.1424501421
 
< 0.1%
0.1462971261
 
< 0.1%
0.1944444441
 
< 0.1%
0.2170138891
 
< 0.1%
ValueCountFrequency (%)
97.980225051
< 0.1%
97.697050951
< 0.1%
97.255203031
< 0.1%
97.168150591
< 0.1%
97.160908831
< 0.1%
97.022995931
< 0.1%
96.826191381
< 0.1%
96.679221371
< 0.1%
96.622060021
< 0.1%
96.542736541
< 0.1%

Social Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13267
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.51962434
Minimum0.399627341
Maximum97.83313378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-09-26T13:40:51.275008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.399627341
5-th percentile12.28033436
Q124.72058224
median37.22906017
Q353.72384838
95-th percentile79.73992001
Maximum97.83313378
Range97.43350644
Interquartile range (IQR)29.00326613

Descriptive statistics

Standard deviation20.50409466
Coefficient of variation (CV)0.5060287451
Kurtosis-0.3354715709
Mean40.51962434
Median Absolute Deviation (MAD)13.92098172
Skewness0.5699522791
Sum543206.0839
Variance420.4178977
MonotonicityNot monotonic
2022-09-26T13:40:51.362027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.068697956
 
< 0.1%
28.953331056
 
< 0.1%
28.965558256
 
< 0.1%
33.252431715
 
< 0.1%
27.030092794
 
< 0.1%
26.119451644
 
< 0.1%
25.916863394
 
< 0.1%
23.843056714
 
< 0.1%
23.90393044
 
< 0.1%
25.337263474
 
< 0.1%
Other values (13257)13359
99.6%
ValueCountFrequency (%)
0.3996273411
< 0.1%
0.4534904131
< 0.1%
0.6288554831
< 0.1%
0.6346144871
< 0.1%
0.6976744191
< 0.1%
0.7740939861
< 0.1%
0.8804347831
< 0.1%
1.0105546821
< 0.1%
1.0234486332
< 0.1%
1.0699526351
< 0.1%
ValueCountFrequency (%)
97.833133781
< 0.1%
97.693704961
< 0.1%
97.668742951
< 0.1%
97.655062681
< 0.1%
97.579012441
< 0.1%
97.394131821
< 0.1%
97.392935251
< 0.1%
97.348331381
< 0.1%
97.256723051
< 0.1%
97.234743751
< 0.1%

Governance Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13249
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.02195931
Minimum0.209118958
Maximum99.49668624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-09-26T13:40:51.449047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.209118958
5-th percentile11.51393206
Q128.87901936
median46.98611754
Q364.8462774
95-th percentile82.62257328
Maximum99.49668624
Range99.28756728
Interquartile range (IQR)35.96725804

Descriptive statistics

Standard deviation22.20718945
Coefficient of variation (CV)0.4722727376
Kurtosis-0.9429094024
Mean47.02195931
Median Absolute Deviation (MAD)17.98712213
Skewness0.01836969661
Sum630376.3866
Variance493.1592634
MonotonicityNot monotonic
2022-09-26T13:40:51.539068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.120354962
 
< 0.1%
20.900127232
 
< 0.1%
51.614127262
 
< 0.1%
24.367581242
 
< 0.1%
76.812935662
 
< 0.1%
75.976843742
 
< 0.1%
43.034637092
 
< 0.1%
18.924694322
 
< 0.1%
71.199862872
 
< 0.1%
39.331114812
 
< 0.1%
Other values (13239)13386
99.9%
ValueCountFrequency (%)
0.2091189581
< 0.1%
0.4833733291
< 0.1%
0.6890641071
< 0.1%
0.7135284141
< 0.1%
0.8130892961
< 0.1%
0.8916454621
< 0.1%
0.9661674991
< 0.1%
1.218940231
< 0.1%
1.2257348861
< 0.1%
1.2346089851
< 0.1%
ValueCountFrequency (%)
99.496686241
< 0.1%
98.610655441
< 0.1%
98.299313361
< 0.1%
98.14167411
< 0.1%
97.52325211
< 0.1%
97.499648791
< 0.1%
97.301438521
< 0.1%
97.028115551
< 0.1%
96.773164111
< 0.1%
96.673930581
< 0.1%

mean-return
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct13392
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001600613576
Minimum-1.770896269
Maximum0.5359062215
Zeros1
Zeros (%)< 0.1%
Negative5638
Negative (%)42.1%
Memory size104.9 KiB
2022-09-26T13:40:51.628088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.770896269
5-th percentile-0.0753132144
Q1-0.01664729007
median0.005759506337
Q30.02496280011
95-th percentile0.06479787727
Maximum0.5359062215
Range2.306802491
Interquartile range (IQR)0.04161009018

Descriptive statistics

Standard deviation0.05085194573
Coefficient of variation (CV)31.77028266
Kurtosis127.8259906
Mean0.001600613576
Median Absolute Deviation (MAD)0.02065356522
Skewness-4.694960651
Sum21.4578256
Variance0.002585920385
MonotonicityNot monotonic
2022-09-26T13:40:51.716108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.031460944612
 
< 0.1%
0.0394704742
 
< 0.1%
0.017488353882
 
< 0.1%
-0.012322317452
 
< 0.1%
-0.059447860672
 
< 0.1%
0.063013380052
 
< 0.1%
0.011522882332
 
< 0.1%
0.015455791742
 
< 0.1%
0.034727601952
 
< 0.1%
0.015445366982
 
< 0.1%
Other values (13382)13386
99.9%
ValueCountFrequency (%)
-1.7708962691
< 0.1%
-0.83730367021
< 0.1%
-0.74955419441
< 0.1%
-0.57379077371
< 0.1%
-0.5520901051
< 0.1%
-0.53041388231
< 0.1%
-0.49257455991
< 0.1%
-0.40940968471
< 0.1%
-0.40191581671
< 0.1%
-0.38965559381
< 0.1%
ValueCountFrequency (%)
0.53590622151
< 0.1%
0.28352657881
< 0.1%
0.25580976621
< 0.1%
0.25518092261
< 0.1%
0.24944734791
< 0.1%
0.24393980941
< 0.1%
0.23766776791
< 0.1%
0.23212046331
< 0.1%
0.23022912211
< 0.1%
0.22828886541
< 0.1%

semi-variance (down)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct13406
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03441228019
Minimum2.049697647 × 10-6
Maximum28.32993695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-09-26T13:40:51.802127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.049697647 × 10-6
5-th percentile0.001251674494
Q10.004002489114
median0.009435781436
Q30.0251679983
95-th percentile0.1080832709
Maximum28.32993695
Range28.3299349
Interquartile range (IQR)0.02116550919

Descriptive statistics

Standard deviation0.3101557243
Coefficient of variation (CV)9.012937316
Kurtosis5703.587574
Mean0.03441228019
Median Absolute Deviation (MAD)0.006852238409
Skewness69.10115461
Sum461.3310282
Variance0.09619657329
MonotonicityNot monotonic
2022-09-26T13:40:51.884146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0040211727321
 
< 0.1%
0.0046066515661
 
< 0.1%
0.04662081951
 
< 0.1%
0.0057540539861
 
< 0.1%
0.0037988482171
 
< 0.1%
0.012764278921
 
< 0.1%
0.02414184081
 
< 0.1%
0.068433290691
 
< 0.1%
0.036863587711
 
< 0.1%
0.073195970841
 
< 0.1%
Other values (13396)13396
99.9%
ValueCountFrequency (%)
2.049697647 × 10-61
< 0.1%
5.055098442 × 10-61
< 0.1%
5.08445488 × 10-61
< 0.1%
5.274323069 × 10-61
< 0.1%
6.131105551 × 10-61
< 0.1%
8.070466148 × 10-61
< 0.1%
1.132803218 × 10-51
< 0.1%
1.166024936 × 10-51
< 0.1%
1.192400629 × 10-51
< 0.1%
1.228339629 × 10-51
< 0.1%
ValueCountFrequency (%)
28.329936951
< 0.1%
15.214790621
< 0.1%
10.603796221
< 0.1%
4.4012740891
< 0.1%
4.1838513351
< 0.1%
2.9907011551
< 0.1%
2.8147659031
< 0.1%
2.6603798661
< 0.1%
2.5282397241
< 0.1%
2.2642717071
< 0.1%

kurtosis
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13406
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6588791642
Minimum-4.925216547
Maximum10.66785002
Zeros0
Zeros (%)0.0%
Negative5999
Negative (%)44.7%
Memory size104.9 KiB
2022-09-26T13:40:51.973166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.925216547
5-th percentile-1.406710549
Q1-0.6411744108
median0.1874007008
Q31.434089442
95-th percentile4.459231328
Maximum10.66785002
Range15.59306656
Interquartile range (IQR)2.075263852

Descriptive statistics

Standard deviation1.850429241
Coefficient of variation (CV)2.808450079
Kurtosis2.501749631
Mean0.6588791642
Median Absolute Deviation (MAD)0.9622094866
Skewness1.441652069
Sum8832.934075
Variance3.424088374
MonotonicityNot monotonic
2022-09-26T13:40:52.058185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.63282574091
 
< 0.1%
-0.60000836651
 
< 0.1%
-1.255168051
 
< 0.1%
3.2319801371
 
< 0.1%
-0.88176868151
 
< 0.1%
-1.7885351121
 
< 0.1%
0.74240174211
 
< 0.1%
0.70194049781
 
< 0.1%
-0.50227993851
 
< 0.1%
-0.29516000671
 
< 0.1%
Other values (13396)13396
99.9%
ValueCountFrequency (%)
-4.9252165471
< 0.1%
-4.7716903051
< 0.1%
-3.9938038021
< 0.1%
-3.7169041821
< 0.1%
-3.3034801381
< 0.1%
-3.2836624931
< 0.1%
-3.2350173981
< 0.1%
-3.0102317641
< 0.1%
-2.916909521
< 0.1%
-2.7756234541
< 0.1%
ValueCountFrequency (%)
10.667850021
< 0.1%
10.396198261
< 0.1%
10.392304821
< 0.1%
10.284360641
< 0.1%
10.022768381
< 0.1%
9.9911235261
< 0.1%
9.9431551851
< 0.1%
9.5758613451
< 0.1%
9.4155301991
< 0.1%
9.4148114551
< 0.1%

skew
Real number (ℝ)

HIGH CORRELATION

Distinct13405
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1385345219
Minimum-3.243905543
Maximum3.149891009
Zeros2
Zeros (%)< 0.1%
Negative7598
Negative (%)56.7%
Memory size104.9 KiB
2022-09-26T13:40:52.150206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.243905543
5-th percentile-1.629396871
Q1-0.6952890294
median-0.1368064219
Q30.4224242633
95-th percentile1.309867741
Maximum3.149891009
Range6.393796553
Interquartile range (IQR)1.117713293

Descriptive statistics

Standard deviation0.8832119049
Coefficient of variation (CV)-6.375392163
Kurtosis0.2994547921
Mean-0.1385345219
Median Absolute Deviation (MAD)0.5587610656
Skewness-0.008493066362
Sum-1857.1938
Variance0.7800632689
MonotonicityNot monotonic
2022-09-26T13:40:52.230225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
< 0.1%
0.061141406491
 
< 0.1%
1.1519642531
 
< 0.1%
1.4809223841
 
< 0.1%
-0.22335785971
 
< 0.1%
-0.13669650511
 
< 0.1%
-1.1047926761
 
< 0.1%
-0.94964337951
 
< 0.1%
0.23101639741
 
< 0.1%
-0.28571320861
 
< 0.1%
Other values (13395)13395
99.9%
ValueCountFrequency (%)
-3.2439055431
< 0.1%
-3.1926246111
< 0.1%
-3.1921451011
< 0.1%
-3.1637537321
< 0.1%
-3.1155757781
< 0.1%
-2.9903263571
< 0.1%
-2.9801153681
< 0.1%
-2.957153091
< 0.1%
-2.915910351
< 0.1%
-2.8693723961
< 0.1%
ValueCountFrequency (%)
3.1498910091
< 0.1%
3.1181397791
< 0.1%
3.0263330941
< 0.1%
3.0012542781
< 0.1%
2.9395501841
< 0.1%
2.9006242261
< 0.1%
2.8469504781
< 0.1%
2.8150819581
< 0.1%
2.7816161991
< 0.1%
2.7276243631
< 0.1%

VaR (95%)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13401
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1761616532
Minimum-6.403826316
Maximum0.05449763172
Zeros1
Zeros (%)< 0.1%
Negative13369
Negative (%)99.7%
Memory size104.9 KiB
2022-09-26T13:40:52.317243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6.403826316
5-th percentile-0.4616766382
Q1-0.223226328
median-0.1313633892
Q3-0.07565988634
95-th percentile-0.03017432282
Maximum0.05449763172
Range6.458323948
Interquartile range (IQR)0.1475664416

Descriptive statistics

Standard deviation0.1807383212
Coefficient of variation (CV)-1.025979933
Kurtosis233.0781805
Mean-0.1761616532
Median Absolute Deviation (MAD)0.06699855294
Skewness-9.324335904
Sum-2361.623123
Variance0.03266634074
MonotonicityNot monotonic
2022-09-26T13:40:52.400262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.34657359033
 
< 0.1%
-0.26531412552
 
< 0.1%
-0.2503876442
 
< 0.1%
-0.23722898982
 
< 0.1%
-0.071075468581
 
< 0.1%
-0.37666329371
 
< 0.1%
-0.088136451481
 
< 0.1%
-0.12806839151
 
< 0.1%
-0.34137384121
 
< 0.1%
-0.14786967121
 
< 0.1%
Other values (13391)13391
99.9%
ValueCountFrequency (%)
-6.4038263161
< 0.1%
-5.7564627321
< 0.1%
-5.0421780381
< 0.1%
-3.4538776391
< 0.1%
-3.1783278781
< 0.1%
-2.1719027111
< 0.1%
-1.9927252371
< 0.1%
-1.7856851141
< 0.1%
-1.6699980661
< 0.1%
-1.644567561
< 0.1%
ValueCountFrequency (%)
0.054497631721
< 0.1%
0.051156618591
< 0.1%
0.032876869491
< 0.1%
0.030358517291
< 0.1%
0.027034805881
< 0.1%
0.017692728661
< 0.1%
0.0164529631
< 0.1%
0.015529174941
< 0.1%
0.013517765121
< 0.1%
0.011234613421
< 0.1%

D(ESG, VaR)
Real number (ℝ≥0)

ZEROS

Distinct11115
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8268450072
Minimum0
Maximum68.67140331
Zeros2292
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-09-26T13:40:52.491283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01158233415
median0.2224363687
Q30.8891084195
95-th percentile3.443560257
Maximum68.67140331
Range68.67140331
Interquartile range (IQR)0.8775260854

Descriptive statistics

Standard deviation1.892817315
Coefficient of variation (CV)2.289204504
Kurtosis223.0252267
Mean0.8268450072
Median Absolute Deviation (MAD)0.2224363687
Skewness10.32378911
Sum11084.68417
Variance3.582757387
MonotonicityNot monotonic
2022-09-26T13:40:52.580303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02292
 
17.1%
0.33612533071
 
< 0.1%
1.5198521811
 
< 0.1%
0.78996742091
 
< 0.1%
0.30908418571
 
< 0.1%
0.26718517291
 
< 0.1%
0.3055186271
 
< 0.1%
0.25317763411
 
< 0.1%
0.39337729831
 
< 0.1%
0.054819882421
 
< 0.1%
Other values (11105)11105
82.8%
ValueCountFrequency (%)
02292
17.1%
7.201370445 × 10-81
 
< 0.1%
7.65170932 × 10-81
 
< 0.1%
1.615828905 × 10-71
 
< 0.1%
2.356886796 × 10-71
 
< 0.1%
3.591858258 × 10-71
 
< 0.1%
5.596244496 × 10-71
 
< 0.1%
7.547698111 × 10-71
 
< 0.1%
1.028963198 × 10-61
 
< 0.1%
1.053538485 × 10-61
 
< 0.1%
ValueCountFrequency (%)
68.671403311
< 0.1%
47.280222291
< 0.1%
47.011040471
< 0.1%
37.174199171
< 0.1%
35.044973961
< 0.1%
28.141407191
< 0.1%
25.853308391
< 0.1%
25.853121291
< 0.1%
24.470680791
< 0.1%
23.23145431
< 0.1%

Interactions

2022-09-26T13:40:49.237547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.383545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.395774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.193955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.019141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.849329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.682517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.774764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.602952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.533163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.402358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.316565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.468565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.469791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.268971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.100159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.926346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.759535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.856783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.682970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.624183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.484377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.391582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.539580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.539807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.339988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.173176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.998363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.831551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.930800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.757987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.704201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.554393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.469599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.614597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.623826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.412004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.248193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.071379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.906568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.005816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.837005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.785219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.632411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.547617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.689614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.695842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.484019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.321210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.146396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.981585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.079833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.915022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.863237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.707427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.626636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.764631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.766859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.557036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.395226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.220413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.061603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.153850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.997041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.939254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.781444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.700652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.836648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.834873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.630053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.467242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.293430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.137620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.224866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.075058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.014271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.853460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.777670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.908664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.904889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.705070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.542259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.371447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.214638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.299883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.165079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.089288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.929478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.859688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:40.984681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.975905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.785088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.619277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.453465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.292655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.376901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.250098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.168305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.007495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.936705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.058698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.047921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.862105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.694294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.528482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.369673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.450917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.337117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.245323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.082512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:50.017724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:41.318757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.119938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:42.940123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:43.772312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:44.604499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:45.696746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:46.525934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:47.440141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:48.324340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-26T13:40:49.158529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-26T13:40:52.660322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-26T13:40:52.775347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-26T13:40:52.885372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-26T13:40:52.998398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-26T13:40:50.142751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-26T13:40:50.291786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

SymbolYearESG ScoreEnvironmental Pillar ScoreSocial Pillar ScoreGovernance Pillar Scoremean-returnsemi-variance (down)kurtosisskewVaR (95%)D(ESG, VaR)
0360.AX2020.027.0810188.91666743.01514418.8092710.0328460.004021-0.6328260.061141-0.0710750.000000
1360.AX2021.031.4109588.45360845.97221025.7593070.0258250.0058391.742724-0.646043-0.0752980.008210
2A.N2017.087.59506577.06065892.79237585.6679960.0355120.0070120.4557900.720648-0.0965250.000000
3A.N2018.089.48925378.04573794.20181788.624684-0.0610430.0044510.6103080.850044-0.1626270.250266
4A.N2019.088.33085579.33599794.50527384.398806-0.0292690.0160820.126243-0.828252-0.2284900.124653
5A.N2020.087.57748979.95897793.59937083.2039840.0456530.2709804.900414-1.988309-0.5202270.691122
6AA.N2016.087.18660585.83233081.59038698.2993130.0262760.0052540.036191-0.293729-0.0632960.000000
7AA.N2017.087.27310990.33374879.47214995.671099-0.0217860.0023200.566293-0.682118-0.0811010.060957
8AA.N2018.086.61997888.38353779.12899596.369097-0.0337960.006287-0.703649-0.791101-0.1379310.290054
9AA.N2019.088.07882587.34716383.47424796.673931-0.0396990.0151112.266191-1.274915-0.2232850.216216

Last rows

SymbolYearESG ScoreEnvironmental Pillar ScoreSocial Pillar ScoreGovernance Pillar Scoremean-returnsemi-variance (down)kurtosisskewVaR (95%)D(ESG, VaR)
13396ZWS.N2017.021.7566034.25006027.08554135.777625-0.0290820.1070022.814027-1.311767-0.4914960.894813
13397ZWS.N2018.022.9664534.19339824.53159843.442041-0.1276200.074135-1.053860-0.274952-0.4616630.013628
13398ZWS.N2019.042.38505947.92359436.35408243.6460980.0129170.0354426.0092752.079474-0.2964571.114489
13399ZWS.N2020.059.56629853.79715376.73664343.977042-0.0384990.057422-0.321493-0.129591-0.3930050.003407
13400ZWS.N2021.059.80939170.17463676.56941925.396839-0.0174650.0105180.3711961.074856-0.1827110.592885
13401ZYNE.OQ2019.017.9050760.00000019.76201428.1499150.0461650.038342-0.011275-0.600594-0.2023720.000000
13402ZYNE.OQ2020.019.7211290.00000035.12339118.8353770.0306290.065104-1.1076780.150942-0.2860200.062175
13403ZYXI.OQ2019.029.4070350.00000037.95658631.873410-0.0323300.0081830.714418-0.798616-0.1514930.000000
13404ZYXI.OQ2020.035.0357580.00000038.17430747.2533570.0128250.0187373.0203271.159771-0.2503880.107150
13405ZYXI.OQ2021.025.1395740.00000033.59673825.7361110.0842390.0364833.264759-1.726963-0.1194020.166947